Real time prediction for converter gas tank levels based on multi-output least square support vector regressor

Abstract

Linz Donawitz converter gas (LDG) is the significant secondary energy resource that plays a crucial role in the energy system of steel industry. Since the real-time prediction for the gas tank level of LDG system is the foundation of energy balance scheduling that directly affects the energy costs of enterprise, more and more attentions has been paid to this issue. In this study, taking the LDG system of Ma’anshan Steel Co., Ltd, China into account, a multi-output least square support vector regressor is proposed, which considers not only the single fitting error of each tank level but also the combined one. Then, a prediction model for the multi-tank LDG system is derived, and a particle swarm optimization is designed to determine the parameters of this model for the sake of improving the prediction accuracy. The experimental results based on the real data from the plant demonstrate that the proposed method is effective to the practical application.